SegFormer3D: Improving the Robustness of Deep Learning Model-Based Image Segmentation in Ultrasound Volumes of the Pediatric Hip
Bibliographic record
Abstract
Developmental dysplasia of the hip (DDH) is a painful orthopedic malformation diagnosed at birth in 1-3% of all newborns. Left untreated, DDH can lead to significant morbidity including long-term disability. Currently the condition is clinically diagnosed using 2-D ultrasound (US) imaging acquired between 0 and 6 mo of age. DDH metrics are manually extracted by highly trained radiologists through manual measurements of relevant anatomy from the 2-D US data, which remains a time-consuming and highly error-prone process. Recently, it was shown that combining 3-D US imaging with deep learning-based automated diagnostic tools may significantly improve accuracy and reduce variability in measuring DDH metrics. However, the robustness of current techniques remains insufficient for reliable deployment into real-life clinical workflows. In this work, we first present a quantitative robustness evaluation of the state of the art in bone segmentation from 3-D US and demonstrate examples of failed or implausible segmentations with convolutional neural network and vision transformer models under common data variations, e.g., small changes in image resolution or anatomical field of view from those encountered in the training data. Second, we propose a 3-D extension of SegFormer architecture, a lightweight transformer-based model with hierarchically structured encoders producing multi-scale features, which we show to concurrently improve accuracy and robustness. Quantitative results on clinical data from pediatric patients in the test set showed up to 0.9% improvement in Dice score and up to a 3% smaller Hausdorff distance 95% compared with state of the art when unseen variations in anatomical structures and data resolutions were introduced.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".